空间数据处理的应用

1. 空间数据处理的应用 移动对象管理

2. Content • Moving object management system • Index structure for Moving object • Query process for moving object • Continuous query of moving object in road network

3. Reference papers • Sunil Prabhaker, Yuni Xia, etc., Query indexing and velocity constrained indexing: scalable Techniques for continuous queries on moving objects, IEEE Transactions on computers, vol.51,no.10,2002 • Y. Tao, C. Faloutsos, D. Papadias, B. Liu , Prediction and Indexing of Moving Objects with Unknown Motion Patterns, SIGMOD 2004 • Yufei Tao, Dimitris Papadias, Jian Zhai, Qing Li, Venn Sampling: A Novel Prediction Technique for Moving Objects, ICDE 2005 • Xiaoyuan Wang and Wei Wang, Continuous Expansion: Efficient Processing of Continuous Range Monitoring in Mobile Environments，DASFAA 2006 • K. Mouratidis, M.L. Yiu, D. Papadias, N. Mamoulis, Continuous Nearest Neighbor Monitoring in Road Networks, VLDB 2006 • Kostas Tzoumas，Man Lung Yiu，Christian S. Jensen，Workload Aware Indexing of Continuously Moving Objects，VLDB2009

4. 移动对象管理的背景 • 移动设备的广泛使用 • 手持设备和移动设备的发展 • 到2005年，大部分的设备可以感知位置信息 • GPS • 面向位置信息的服务的需求 • 智能交通 • 大型活动的管理

5. 移动对象管理的特征 • Coordinate-based Queries • point, • range • nearest-neighbor queries • Trajectory-based Queries • topological queries: enter, leave, cross, and bypass • navigational queries using derived information, e.g., speed • Continuous Queries • Challenge • Continuous update of the position

6. 移动对象管理系统的体系结构（集中式）

7. 移动对象管理系统的体系结构（分布式） • 移动对象具有一定的计算能力 • 移动对象了解查询的集合，当对象移动过了边界的时候才向中心报告 • 中心接到报告后修改相应的查询结果

8. 移动对象索引结构和查询的主要问题 • 对象的移动性 • 大规模的修改操作 • 支持实时查询 • MBR的变动方法 • 对象移动方向的预测 • 大量连续查询的处理 • 查询的有效管理

9. Answer Query Query • Continuous Queries Answer Query Data Data Snapshot vs. Continuous Query Processing • Traditional (Snapshot) Queries Data

10. 查询的索引结构 • 应用场景 • 大量持续查询 • 主要思路 • 固定查询，监控对象的移动 • 面向查询的索引结构 • 查询的表示形式：矩形框 • 基本索引：R_树

11. 面向查询的索引结构 • 操作方法 • 初始 • 每个对象计算其所在的查询。 • 增加新的查询 • 放入索引结构 • 计算包含的对象 • 时间变化 • 监测移动对象的位置变化 • 计算查询结果的变化

12. Query View • A query view is a combined query consisting of one or more continuous queries q1, ..., qi. The monitoring region of the query view, called the view region, is defined as the intersection of the monitoring regions of q1, ..., qi • In the Figure, there are five query views v1 to v5, which correspond to {q1}, {q1, q2}, {q2}, {q2,q3} and {q3}

13. Query View • For the construction of query views, we take “splitting-merging” strategy • Divide the space into small grid cells of same size and map all the monitoring regions on the grid space. Then we collect all the grid cells, and merge those that are covered by the same query regions. • Query view abstracts the dynamic relation between the objects’ movements and the query answers • The query answers are changed if and only if there are objects changing their query views • CEM incurs the minimum transmission cost on the moving object side

14. Data Structure • On the server side • Query View Table (QVT) • To store the query view id, its view region, and the corresponding query list • Grid-based View Index (GVI) • A grid structure to index the query views • Query Table (QT) • To organize the queries • Query Result Buffer (QRB) • To store the incoming results

15. Data Structure • On the mobile side • Query View Region (QVR) • To represent the moving object’s current view region • Grid-based Location Index (GLI) • An auxiliary structure to fast determine whether the current location is within the view region

16. SafeRect and SafeSphere

17. Computing the Safe Regions • If object O is contained in a query, choose one such query rectangle and determine the relevant intersecting or contained query rectangles. If object O is not contained in a query rectangle, we consider all query rectangles as relevant. Let E be the set of relevant query rectangles. • Take object O as the origin and determine which relevant rectangles lie in which of the four induced quadrants. For each quadrant, sort the corner vertices of query rectangles that fall into this quadrant. For each quadrant determine the dominating points. • The dominating points create a staircase for each quadrant. Use the staircases to find the empty rectangle with the maximum area (using the property that a largest empty rectangle touches at least one corner of the four staircases).

18. Index structure for moving object • 基本方法 • R_树 • Space Filling Curves • 基本假设 • 速度有限 • 主要困难 • 大量的修改 • 解决方法 • 变化MBR的区域 • 查询的范围

19. Index structure for moving object

20. Index structure for moving object • Adjust the size of MBR

21. Index structure for moving object • Representation of the object • Shrink and grow the size of the rectangle in different work load

22. Index structure for moving object • 优化方法 • 簇聚 • 思路：对象存储在磁盘上，通过对对象移动过程中进行簇聚提高效率。 • 对查询进行聚类 • 减少对同一对象的多次访问 • 重构 • 重构整个索引 • 刷新MBR的范围 • 基于深度优先的方法

23. Index structure for moving object • 优化方法 • 结点分割

24. Updating Index Structures • Traditional R-tree updates are top-down • Updates translated to delete and insert transactions • To support frequent updates: • Updates can be managed in space without the need for deletion or insertions • Bottom-up approaches through auxiliary index structures to locate the object identifier Hash based on OID

25. Update Memo Update Memos Spatio-temporal Queries • Keep a memo with the R-tree • The memo contains the recent updates to the existing R-tree • The query answer returned from the R-tree should be passed through the memo • The update memo is reflected to the R-tree once the relevant disk page is retrieved Raw answer set Final answer set

26. Space Filling Curves based Index • Bx树 • 可以同传统的关系数据库系统相结合 • 基于Hilbert curves

27. 对对象的移动方向进行预测 • 目标 • 了解对象的运动方向 • 对运动方向类似的对象进行聚类 • 减小MBR的规模，提高查询效率 • 方法 • 基于更多类型的运行轨迹 • 基于动态调整

28. 移动类型 . . .

29. 基本想法 • 基于对象的当前位置，预测其未来的位置 • 当前时刻的位置 • ===> • 下一时刻的位置

30. 线性活动 • o(t) = o(0) + vt • o(t) = o(t1) + [o(t1)  o(t2)] = 2o(t1)  o(t2) • o(t1), o(t2) ===> o(t) • 获取对象的移动方向和速度

31. 获得加速度 • o(t) = o(0)+ v t + ½at2 • o(t)=3o(t1)3o(t2)+o(t3) • o(t1), o(t2), o(t3) ===> o(t). • 获得速度和加速度.

32. 圆形轨迹 • o(t1), o(t2) ===> o(t). • 获取半径.

33. 基于预测的体系结构

35. Continuous Nearest Neighbor Monitoring in Road Networks • Continuous NN monitoring: Long-running queries demand constant update of their results. • Existing methods are designed for Euclidean spaces. • Consider a road network(where edge weights correspond to their length, or travel time). Queries and objects move in the network. • Network distance: the length (i.e., sum of weights) of the shortest path connecting them. (Example: taxi – pedestrians)

36. Continuous NN monitoring in a Road Network • Queries and objects move in an unpredictable manner in the network, issuing an update whenever they move • Network edges issue weight updates • Central server processes the stream of updates, and continuously reports the k NNs of each query according to network distance • Aim: to minimize the query processing time

37. Data Structure • Edges: • indexed with a quadtree. • the objects in it, • an influence list. • Queries • its current NNs • expansion tree.

38. Initial result (k=3): expansion tree, infl. intervals, and marks Only updates affecting the expansion tree can alter the result! Store q in influence lists of affecting edges IMA（INCREMENTAL MONITORING ALGORITHM）: Initial NN computation

39. Outgoing no more than incoming NNs: update result + shrink expansion tree IMA: Object updates (Case 1)

40. New (shrunk) expansion tree IMA: Object updates (Case 1)

41. More outgoing than incoming: re-compute from marks + expand tree IMA: Object updates (Case 2)

42. New (grown) expansion tree IMA: Object updates (Case 2)

43. Re-compute starting from valid tree marks IMA: Query updates

44. IMA: Edge updates - Weight increase

45. IMA: Edge updates - Weight decrease

46. IMA: Multiple concurrent updates Order of updates: Decreasing edge weights Increasing edge weights Query movement Object updates For each affected query q NN set= k best among remaining NNs and incomers Expand from marks in valid expansion tree

47. GMA: Main idea Sequence: path between consecutive intersection or terminal nodes The k-NN set of any query in sequence s is in the union of (i) the objects in s, (ii) the k-NNs of its intersection nodes (endpoints).

48. GMA: Main idea (example) Objects on sequence between n1 and n5 = {p4, p5} 2-NNs of intersection n1 = {p1, p5} 2-NNs of intersection n5 = {p3, p2} 2-NNs of q1 or q2 {p4, p5} {p1, p5}  {p3, p2}

49. GMA: Active nodes • active node: intersection node of nonempty sequence • GMA monitors the k-NNs of active nodes (using IMA), and uses them to compute the NNs of the actual user queries • GMA reduces CPU time by • shared execution among queries in the same sequence • reduction from NN monitoring of moving queries to NN monitoring of static active nodes.

50. GMA: Update processing • Initial Result: utilizing active node NNs • NN Maintenance: In every processing cycle do: • Update NNs of active nodes with IMA. • If NNs of active node n change, re-compute affected queries in sequences adjacent to n • If object/edge updates occur in sequence s, re-compute affected queries within sequence s • Re-compute moving queries